Using permutations for hierarchical clustering of time series
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Knowledge AreaEstadística e Investigación Operativa; Matemática Aplicada
SponsorsAuthors have been partially supported by the Grant MTM2017-84079-P from Agencia Estatal de Investigacion (AEI) y Fondo Europeo de Desarrollo Regional (FEDER) and the Ministerio de Economia, Industria y Competitividad (Agencia Estatal de Investigacion, Spanish Government) under research project ENE-2016-78509-C3-2-P, and EU FEDER funds.
Realizado en/conUniversidad Politécnica de Cartagena
Bibliographic CitationCánovas JS, Guillamón A, Ruiz-Abellón MC. Using Permutations for Hierarchical Clustering of Time Series. Entropy. 2019; 21(3):306. https://doi.org/10.3390/e21030306
KeywordsTime series clustering
Time series dependency
Two distances based on permutations are considered to measure the similarity of two time series according to their strength of dependency. The distance measures are used together with different linkages to get hierarchical clustering methods of time series by dependency. We apply these distances to both simulated theoretical and real data series. For simulated time series the distances show good clustering results, both in the case of linear and non-linear dependencies. The effect of the embedding dimension and the linkage method are also analyzed. Finally, several real data series are properly clustered using the proposed method.
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